The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Some previous efforts have been placed on object recognition technologies, especially technologies involving the use of a camera-equipped mobile device. Some exemplary techniques can be found in co-owned U.S. Pat. Nos. 7,016,532; 8,224,077; 8,224,078; and 8,218,873.
All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
Unfortunately known techniques utilized for object recognition generally rely on analysis of grayscale images. While these technologies can be utilized to recognize a wide range of objects, it has shown to create problems in attempts to recognize objects that have little color variation (hereinafter referred as “monochrome objects”), especially where representations of such objects are captured under different lighting environments. For example, toys from various brands might have numerous human recognizable parts that cast or molded in same color plastics. When an image of the toy is converted to grayscale, the recognizable features are lost and not readily recognizable by image analysis algorithms because the shades of color are converted to the same shade or similar shades of gray.
More recently, some efforts have been placed on recognition of objects having little color variation.
An example of such efforts can be found in WO 2013/056315 to Vidal Calleja, which describes extracting features from training images, and clustering the features into groups of features termed visual words. Vidal Calleja also generally describes converting an image from an RGB color space a HSV color space.
Additional examples include U.S. Patent Application Publication No. 2011/0064301 to Sun describes a concatenation of a scale-invariant feature transform (SIFT) descriptor and HSV color; U.S. Patent Application Publication No. 2011/0316963 to Li describes fast and real-time stitching of video images to provide users with 3D panoramic seamless conferences of better luminance and hue effects by rectifying the color of the video images; U.S. Patent Application Publication No. 2012/0274777 to Saptharishi describes a system to track objects based on features that can include hue; and U.S. Patent Application Publication No. 2012/0147163 to Kaminsky describes changing a color map to enhance differences for color challenged users.
Another example can be found in “A Bag-Of-Features Approach Based On Hue-SIFT Descriptor For Nude Detection” by Lopes et al., which discusses the need to filter improper images from visual content by using a “Hue-SIFT,” a modified SIFT descriptor with added color histogram information. Unfortunately, this requires modification of the standard SIFT descriptors themselves, which increases processing time due to additional calculation. A more useful solution, as described by the applicant's work below, would leverage unmodified off the shelf algorithms and their unmodified descriptors while increasing object resolution power.
None of the above references appear to quickly and efficiently recognize monochrome objects. Thus, there is still a need for improved metric-based recognition systems and methods.